Improved Eigenfeature Regularization for Face Identification

Mandal, Bappaditya

Abstract

In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analysis on within-class and within-subclass eigenspectrums of face images and propose a novel method of eigenspectrum modeling which extracts discriminative features of faces from both within-subclass and total or between-subclass scatter matrices. Effective low-dimensional face discriminative features are extracted for face recognition (FR) after performing discriminant evaluation in the entire eigenspace. Experimental results on popular face databases (AR, FERET) and the challenging unconstrained YouTube Face database show the superiority of our proposed approach on all three databases.

Citation

Mandal, B. (2015, September). Improved Eigenfeature Regularization for Face Identification. Paper presented at ICIP 2015 - IEEE International Conference on Image Processing

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: ICIP 2015 - IEEE International Conference on Image Processing
• Start Date: Sep 27, 2015
• End Date: Sep 30, 2015
• Deposit Date: Nov 21, 2023
• Public URL: https://keele-repository.worktribe.com/output/642901
• Publisher URL: https://arxiv.org/abs/1602.03256
• Related Public URLs: https://www.icip2015.org/; https://ieeexplore.ieee.org/xpl/conhome/7328364/proceeding